Artificial Intelligence for Energy Management

Harness the future of sustainable energy with this essential volume, which provides a comprehensive guide to integrating artificial intelligence for efficient energy storage and management systems. To achieve a clean and sustainable energy future, renewable energy sources such as solar, hydropower, and wind must develop dependable and effective energy storage technologies. The growing need for intelligent energy storage systems is greater than ever, despite substantial advancements in sophisticated energy storage technology, especially for large-scale energy storage. This book aims to provide the most recent developments in the integration of artificial intelligence for energy storage and management systems by introducing energy systems, power generation, and power needs to reduce expenses associated with generation, power loss, and environmental impacts. It explores state-of-the-art methods and solutions, such as intelligent wind and solar energy systems, founded on current technology, offering a strong foundation to satisfy the requirements of both developed and developing nations. An extensive overview of the many kinds of storage options is included. Additionally, it examines how utilizing diverse storage types can enhance the administration of a power supply system while also considering the more significant opportunities that result from integrating multiple storage devices into a system. Artificial Intelligence for Energy Management is a collection of expert contributions encompassing new techniques, methods, algorithms, practical solutions, and models for renewable energy storage based on artificial intelligence.

Preface xvii 1 Introduction to Next-Generation Energy Management and Need for AI Solutions 1 D. Gunapriya, P. Vinoth Kumar, G. Banu, S. Revathy, S. Giriprasad and N. Pushpalatha 1.1 Introduction 2 1.1.1 Challenges in Traditional Energy Management 3 1.1.2 Emergence of Next-Generation Energy Management 3 1.2 Application of AI in Energy Management Revolution 5 1.3 AI in Energy Sector 6 1.3.1 AI in Energy Optimization 6 1.3.2 Data Analytics and Predictive Maintenance 6 1.3.3 Intelligent Energy Storage and Demand Response 6 1.4 Role of AI in Energy Efficiency Improvement 7 1.4.1 Smart Building Management and Automation 7 1.4.2 AI-Driven Energy Analysis and Optimization 7 1.5 Role of AI in Demand Forecasting and Load Balancing 7 1.5.1 AI-Based Effective Forecasting of Energy Balancing 8 1.5.2 AI-Based Load Balancing 8 1.6 Enhanced Sustainability and Reduced Carbon Footprint 8 1.7 AI-Based Grid Stability Enhancement 8 1.7.1 AI-Driven Grid Monitoring and Control 8 1.7.2 AI-Based Intelligent Fault Detection 9 1.8 Predictive Maintenance and Asset Management 9 1.8.1 Role of AI in Predictive Maintenance 9 1.8.2 Optimizing Asset Management with AI 9 1.9 AI-Powered Energy Trading and Price Optimization 9 1.9.1 Revolutionizing Energy Trading with AI 9 1.9.2 Price Optimization Using AI for Energy Management 10 1.10 Ethical Considerations in AI-Powered Energy Management 10 1.10.1 Enhancing Energy Efficiency 10 1.10.2 Mitigating Environmental Impact 11 1.10.3 Empowering Consumers 11 1.10.4 Data Privacy and Security Concerns 11 1.10.5 Economic Implications 12 1.10.6 Ethical Considerations 12 1.10.7 Workforce Disruption and Reskilling 13 1.11 Challenges in Incorporating AI in EMS 13 1.12 Case Studies on Implementing AI for Future Energy Management 18 1.12.1 Case Study 1: Smart Grid Implementation in User’s Utility Company 18 1.12.2 Case Study 2: AI-Driven Energy Management in User’s Manufacturing Facility 19 1.12.3 Case Study 3: AI-Powered Demand Response Program in a Smart City 20 1.13 Future Research Directions 21 1.13.1 Track for Future Trends and Innovation 21 1.13.2 The Importance of Cooperation and Financial Contribution to AI Research and Development 21 1.13.3 Contribution of AI in Achieving Energy Transient Objective 22 1.13.4 Developments in Energy Management and AI 22 1.13.5 Rules and Guidelines for AI in Energy Management 22 1.13.6 Decision-Making Transparency and Accountability 22 1.13.7 AI’s Potential to Revolutionize the Power Sector 23 1.14 Conclusion 23 References 23 2 Overview of Innovative Next Generation Energy Storage Technologies 27 D. Magdalin Mary, G. Sophia Jasmine, V. Vanitha, C. Kumar and T. Dharma Raj 2.1 Introduction 28 2.2 Energy Storage Techniques 29 2.3 Mechanical Energy Storage System 35 2.4 Electrochemical Storage System 35 2.5 Thermal Storage System 36 2.6 Electrical Energy Storage System 37 2.7 Hydrogen Storage System (Power-to-Gas) 37 References 37 3 Battery Energy Storage Systems with AI 39 Ashadevi S. and Latha R. 3.1 Introduction 39 3.2 System for Managing Batteries 41 3.2.1 State Estimation 44 3.2.1.1 State of Charge 44 3.3 Demand Response Strategies 52 3.4 Battery Energy Storage System 53 3.5 Technical Overview of Battery Energy Storage System 54 3.5.1 WSN in Battery Energy Storage 54 3.5.2 IoT in Battery Energy Storage 56 3.5.3 Cloud Computing in Battery Energy Storage 56 3.5.4 Big Data in Battery Energy Storage 57 3.5.5 Artificial and Machine Learning Approaches in Battery Energy Storage 58 3.5.6 Taxonomy of Cyber Security in Energy Storage Systems 60 3.6 Conclusion and Future Scope 60 References 61 4 AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries 65 Sujith M., Pardeshi D.B., Krushna Lad, Pratiksha Ahire and Karun Pagetra 4.1 Introduction 66 4.2 Cathode Material 68 4.2.1 Sodium Iron Phosphate 68 4.3 Anode Material 71 4.3.1 Sodium Titanate (Na2Ti3O7) 71 4.4 Electrolyte 73 4.4.1 NaSICON (Sodium Super Ionic Conductor) 73 4.5 State of Discharge (SOD) 75 4.6 State of Health (SOH) 76 4.7 BMS Algorithm with AI for SOH 77 4.8 Conclusion 79 References 80 5 Design and Development of an Adaptive Battery Management System for E-Vehicles 83 Saravanan Palaniswamy, Anbuselvi Mathivanan, A. Siyan Ananth and Sonu R. 5.1 Introduction 84 5.2 Related Works 85 5.3 Simulation Design 87 5.4 System Design 89 5.5 Implementation 95 5.6 Experimental Results 96 5.7 Conclusion 98 Bibliography 98 6 Remaining Useful Life (RUL) Prediction for EV Batteries 101 Anbuselvi Mathivanan, Saravanan Palaniswamy and M. Arul Mozhi 6.1 Introduction 102 6.1.1 General Consensus 102 6.1.2 Understanding Battery Metrics 103 6.1.2.1 State of Current (SoC) 103 6.1.2.2 State of Health (SoH) 104 6.1.2.3 Rul 104 6.1.3 Objective of the Study 105 6.2 Related Works 105 6.3 Proposed Model 106 6.3.1 Dataset 107 6.3.2 SoC 108 6.3.2.1 Estimation Methods of SoC 109 6.3.2.2 EKF Architecture 110 6.3.2.3 EKF Implementation 111 6.3.3 SoH 112 6.3.3.1 Estimation of SoH 112 6.3.3.2 Lstm 113 6.4 Hardware Implementation 115 6.4.1 Raspberry Pi 4 115 6.4.2 Arduino Uno 116 6.4.3 Current Sensor 116 6.4.4 DHT11 Sensor 118 6.4.5 Voltage Sensor 119 6.5 Outcomes and Analysis 120 6.5.1 Estimation of SoC 120 6.5.2 Analysis of SoH Estimation 121 6.5.3 Prediction of RUL 122 6.5.4 Hardware 123 6.6 Conclusion 124 References 125 7 Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System 129 K. Praharshitha, Varun S., Rithick Sarathi M.B. and V. Indragandhi 7.1 Introduction 129 7.2 Literature Survey 130 7.3 Technical Specification 132 7.4 Methodology 133 7.5 Project Demonstration 133 7.6 Results 135 Acknowledgement 138 References 138 8 An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques 141 V. Vanitha, G. Sophia Jasmine and D. Magdalin Mary 8.1 Introduction 142 8.2 Approaches in Charging Optimization 144 8.3 System Model 145 8.4 Proposed Methodology 146 8.4.1 Process of Proposed C-CObTMPC 147 8.4.2 Optimization with TMPC Model 149 8.4.3 Construction of Powertrain Architecture 150 8.4.4 Optimum Control Strategies 152 8.5 Results and Discussion 153 8.5.1 Stability Verification 154 8.5.2 Performance Analysis 154 8.5.2.1 Torque Analysis 155 8.5.2.2 Operating Time 158 8.5.2.3 Fuel Consumption 159 8.5.2.4 Cost Objective Function 160 8.5.3 Discussion 161 8.6 Conclusion 162 References 162 9 Machine Learning and Deep Learning Methods for Energy Management Systems 165 V. Manimegalai, P. Ravi Raaghav, V. Mohanapriya, T.R. Vashishsdh and S. Palaniappan 9.1 Introduction 166 9.2 Building Energy Management System 167 9.2.1 Roles of Deep Learning and Machine Learning 169 9.2.2 Future Scope 171 9.3 Grid Optimization 173 9.3.1 Role of ML and DL in Grid Optimization 174 9.3.2 Future Scope 183 9.3.3 Conclusion 184 9.4 Intelligent Energy Storage 184 9.4.1 Overview of Energy Storage Technologies 185 9.4.2 Roles of Machine Learning and Deep Learning 187 9.4.3 Energy Storage Optimization 190 9.4.4 Predictive Maintenance 192 9.4.5 Grid Optimization and Demand Response 194 9.4.6 Current Research 197 9.5 Roles of ml and dl 199 9.5.1 Energy Demand Forecasting 200 9.5.2 Future Scope 201 9.6 The Roles of Traditional Methods in Energy Management System 204 9.6.1 The Roles of DL And ML in Energy Management System 206 9.6.2 Future Scopes 208 9.7 Conclusion 209 References 210 10 Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance 213 Deepika Amudala and P. Buchibabu 10.1 Introduction 213 10.2 Modeling of Grid-Tied PV 215 10.3 MATLAB Simulation Design and Results 216 10.3.1 Simulations Results 217 10.4 Comparison of THD (Total Hormonic Distortion) Values Between PI and ANN 222 10.5 Conclusion 223 References 223 11 Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm 225 Karthik Nagarajan, Arul Rajagopalan and Priyadarshini Ramasubramanian 11.1 Introduction 226 11.2 Problem Formulation 227 11.2.1 Connected Microgrid Network 228 11.2.2 Mathematical Modeling of Demand Response 230 11.2.2.1 Cost Function for Customers 231 11.2.3 Model of Demand Response Integrated within a Grid-Connected Microgrid 233 11.3 Enhanced Crayfish Optimization Algorithm 234 11.4 Fuzzy Logic-Based Selection of Optimal Compromise Solution 239 11.5 Results and Discussion 240 11.6 Conclusion 244 References 244 12 Relative Investigation of Swarm Optimized Load Frequency Controller 247 Sheema B. S. P., Peer Fathima A. and Stella Morris 12.1 Introduction 248 12.1.1 Literature Review 249 12.1.2 Contribution 250 12.2 Methodology 250 12.2.1 Modeling of Two-Area Thermal Power Network 250 12.2.2 Particle Swarm Optimization Algorithm 253 12.2.3 PSO-PID Controller Design 254 12.3 Simulation Results and Discussions 257 12.4 Conclusion 261 References 261 13 Economic Aspects and Life Cycle Assessment in Energy Storage Systems 263 Pandiyan P., Senthil Kumar R., Saravanan S. and P. Balakumar 13.1 Introduction 264 13.2 Types of Energy Storage Systems 265 13.2.1 Mechanical Storage 266 13.2.1.1 Pumped Hydro Storage 266 13.2.1.2 Compressed Air Energy Storage (CAES) 267 13.2.1.3 Flywheel Energy Storage (FWES) 267 13.2.2 Electrochemical Storage 267 13.2.2.1 Lead-Acid (LA) Batteries 268 13.2.2.2 Sodium-Sulphur (NaS) Batteries 268 13.2.2.3 Lithium-Ion Batteries 268 13.2.2.4 Nickel-Cadmium (NiCd) Batteries 269 13.2.2.5 Zinc-Bromine (ZnBr) Batteries 269 13.2.2.6 Vanadium Redox (VR) Batteries 270 13.2.3 Hydrogen-Based Energy Storage (HES) 270 13.2.4 Thermal Energy Storage (TES) 270 13.2.5 Supercapacitor Energy Storage (SCES) 271 13.3 Life Cycle Assessment (LCA) in Energy Storage Systems 271 13.3.1 Life Cycle Sustainability Assessment (LCSA) 273 13.3.2 Life Cycle Assessment Framework 274 13.3.3 Life Cycle Inventory of the LRES and VRES 275 13.3.4 Impacts on Human Toxicity 276 13.3.5 Life Cycle Impact Assessment 276 13.4 AI in Economic Optimization and Life Cycle Management (LCA) 277 13.4.1 ANN Based Optimization 278 13.4.2 Optimization Algorithm 279 13.4.3 AI and Machine Learning in LCA 280 13.4.4 Collection of Data and Preprocessing 281 13.4.5 Development of AI/ML Models for LCA 282 13.4.6 Predictive Analysis and Optimization 283 13.5 Challenges and Future Directions 284 13.5.1 Battery Degradation 284 13.5.2 SOC Impact on Energy Storage Systems 284 13.5.3 Economies of Scale 284 13.5.4 Consistency in Cost Estimation 285 13.5.5 Need for Uncertainty Analysis 285 13.5.6 Emerging Energy Storage Technology LCA 285 13.6 Conclusion 286 References 286 14 Energy Monitoring System Using Arduino and Blynk: Design and Simulation 291 Pilla Krishna Satwik, Samartha and Sritama Roy 14.1 Introduction 291 14.2 Motivations 293 14.3 System Architecture 295 14.3.1 Overall System Overview 295 14.3.2 Hardware Components 295 14.3.3 Software Components 295 14.3.4 Communication Protocols 296 14.4 Design and Implementation 297 14.4.1 Sensor Interface and Data Acquisition 297 14.4.2 Arduino Microcontroller Programming 298 14.4.3 Blynk Mobile Application 300 14.4.4 VSPE Configuration 301 14.5 Experimental Evaluation 302 14.6 Conclusion 304 References 305 15 Smart Home Energy Management System 307 A. R. Kalaiarasi, T. Deepa and S. Angalaeswari 15.1 Introduction 307 15.2 Arduino UNO 310 15.2.1 Power Supply 310 15.2.2 Transmitter and Receiver 310 15.3 Bluetooth Module 310 15.4 Relay Module 311 15.5 Android Application 312 15.6 Software 313 15.7 Flow Diagram 313 15.8 Hardware Implementation 314 15.9 Results and Discussion 315 15.10 Conclusion 317 References 317 16 A Study to Analyze the Vulnerabilities and Threats Faced by the Power Sector 319 A. R. Kalaiarasi and Aishwarya G. P. 16.1 Introduction 319 16.2 Analyzing the Risk Index of Threats with Case Study 321 16.2.1 Natural Threats and Impacts 321 16.2.2 Technical Threats and Impact 323 16.2.3 Human Threats and Impacts 325 16.3 Cyber Vulnerabilities of Power System Case Study 326 16.3.1 Architecture of a SCADA System 328 16.3.2 Cyber-Attack Scenarios on SCADA System 329 16.3.2.1 Attacking the Substation 329 16.3.2.2 Malicious Codes 329 16.3.2.3 Accessing RTU by Breaking Protocol 329 16.3.2.4 Attacking the Corruption LAN and Gaining Access to the Substation 330 16.3.3 Methods to Promote Cyber Security 330 16.3.4 Proposed Solution for Threats or Vulnerabilities 331 16.4 Conclusion 332 References 332 17 Integrated Hybrid Energy Management to Reduce Standby Mode Power Consumption 335 N. Amuthan, N. Sivakumar and B. Gopal Samy 17.1 Introduction 336 17.2 Standby Power Regulations and Standards 338 17.2.1 International Efficiency Standards 338 17.2.2 Governmental Regulations on Standby Power 339 17.2.3 Compliance and Enforcement Mechanisms 339 17.3 Theoretical Framework for Standby Power Reduction 340 17.3.1 Principles of Power Conversion Efficiency 340 17.3.2 Theoretical Models for Standby Power Consumption 341 17.3.3 Predictive Analysis for Standby Power Reduction 341 17.4 Energy Harvesting and Standby Power 342 17.4.1 Utilizing Ambient Energy Sources 342 17.4.2 Integration with Renewable Energy Systems 342 17.4.3 Energy Storage and Standby Power Reduction 343 17.5 Power Factor Correction (PFC) and Standby Power 344 17.5.1 Basics of Power Factor and Its Importance 344 17.5.2 PFC Techniques for Reducing Standby Power 344 17.5.3 Impact of PFC on Overall Energy Consumption 345 17.6 Zero Standby Power Solutions 345 17.6.1 Concept and Feasibility of Zero Standby Power 346 17.6.2 Design Challenges for Zero Standby Power Converters 346 17.6.3 Case Studies of Zero Standby Power Applications 347 17.7 Control Strategies for Power Converters 347 17.7.1 Analog vs. Digital Control Methods 348 17.7.2 Predictive Control for Standby Power Reduction 348 17.7.3 Feedback Mechanisms and Efficiency 349 17.8 Software Approaches to Standby Power Reduction 350 17.8.1 Firmware Optimization for Power Converters 350 17.8.2 Algorithmic Solutions for Standby Power Management 351 17.8.3 Software-Based Monitoring and Control Systems 351 17.9 Electromagnetic Interference (EMI) and Standby Power 351 17.9.1 EMI in Power Converters 352 17.9.2 EMI Reduction Techniques and Standby Power 352 17.9.3 Standards and Regulations for EMI in Power Converters 353 17.10 Cost-Benefit Analysis of Standby Power Reduction 353 17.10.1 Initial Costs vs. Long-Term Savings 354 17.10.2 Payback Periods for Energy-Efficient Converters 354 17.10.3 Incentives and Rebates for Adopting Efficient Technologies 355 17.11 Consumer Electronics and Standby Power 355 17.11.1 Prevalence of Standby Power in Consumer Devices 355 17.11.2 Strategies for Consumer Awareness and Behavior Change 356 17.11.3 Industry Initiatives for Reducing Standby Power in Electronics 356 17.12 Integration of IoT Devices with Power Converters 357 17.12.1 IoT for Intelligent Power Management 357 17.12.2 Data Analytics for Standby Power Optimization 358 17.12.3 Security Concerns with IoT-Enabled Power Converters 358 17.13 Policy Implications and Advocacy for Standby Power Reduction 358 17.13.1 Role of Policymakers in Standby Power Reduction 359 17.13.2 Advocacy Groups and their Impact 359 17.13.3 Future Directions for Legislation and Standards 360 17.14 Educational Initiatives for Standby Power Awareness 360 17.14.1 Curriculum Development for Energy Efficiency 361 17.14.2 Public Outreach and Awareness Campaigns 361 17.14.3 Professional Development and Training Programs 362 17.15 Conclusion 362 17.15.1 Summary of Novel Methods for Reducing Standby Power 362 17.15.2 Implications for Future Research and Development 363 17.15.3 Final Thoughts on the Importance of Standby Power Reduction 363 References 364 18 Enhanced Reliability of Electrical Power Transmission in IEEE 24 DC Bus System Using Hybrid Optimization 371 Shereena Gaffoor and Mariamma Chacko 18.1 Introduction 372 18.2 Hybrid Optimization Model Combining GWO and GA 374 18.3 System Description and Model Implementation 375 18.4 Reliability Factors Considered 377 18.4.1 Implications for Future Research in Power System Reliability 381 18.5 Conclusion 382 References 382 19 Impact of Renewable Energy Sources on Power System Inertia 385 M. Chethan, Ravi Kuppan, M. Dharani and M. Kalpana 19.1 Introduction 386 19.2 VSG: Integration, Modeling, and Controller Structure 389 19.3 Simulation Results and Discussion 392 19.4 Conclusion 395 References 396 20 Empowering India Toward Sustainability: An In-Depth Review of Wind Energy Utilization 399 Shibin Shaji John, Heyrin Ann Sony, Ahan Vincent Michael and Sitharthan Ramachandran 20.1 Introduction 400 20.2 Global Status of Wind Energy 401 20.3 Wind Energy Potential in India 404 20.4 Wind Energy Production Capacity in India 405 20.4.1 Wind Energy Status in India 406 20.4.2 Sustained Growth in India’s Wind Energy Market 409 20.5 Indian Wind Energy Policy for Promoting Installation 411 20.6 Conclusion 412 References 412 About the Editors 415 Index 417